TwoStep | Latent Gold | SNOB | |
---|---|---|---|
Method | Distance-based, agglomerative hierarchical cluster analysis | Finite mixture modeling to probabilistically identify latent classes | Finite mixture modeling to probabilistically identify latent classes |
Stopping rule to identify number of subgroups | Automated using either ‘Bayesian information criterion’ or ‘Akaike’s information criterion’ | Analyst choice using various criteria, including ‘Bayesian information criterion’, unexplained variance, Chi-square p-value | Automated using ‘Minimum message length’ principle |
Suitable data types | Ordinal data require recoding as dichotomous or handled as if interval data | All types | All types |
Report classification probability of individuals | No | Yes | Yes |
Sensitivity to subgroups | Least | Middle | Most |
Reproducibility | Very high | Very high | Very high |
Accuracy | Very high | Very high | Very high |
Cost | Most expensive | Less expensive | Free |
Support | Extensive documentation, fee-based support available | Extensive documentation and some free support available | Some documentation but minimal support available |
Interpretability of presentation of results | Results are presented numerically and graphically (charts of certainty of the subgroup structure, bar and pie charts of cluster frequencies, and charts displaying the importance of specific variables to subgroups) | Results are presented numerically and graphically (including a tri-plot displaying the relationships between subgroups) | Results are mostly numeric (although a tree diagram is produced showing the relationship between ‘mother’ and ‘daughter’ subgroups) |
Learning curve (subjective judgement) | Easy | Middle | Hard |